Up-scaling Agent-Based Discrete-Choice Transportation Models using Artificial Neural Networks
89th Annual Meeting, Transportation Research Board of the National Academies, Washington, DC, , 10-3130, 2010
Abstract: Agent based models (ABMs) can be used for simulating consumer transportation discrete choices, while incorporating the effects of heterogeneous agent behaviors and social influences. However, the application of ABMs at large‐scales may be computationally prohibitive (e.g., for millions of agents). In an attempt to harness the modeling capabilities of ABMs at large scales, we develop a recurrent artificial neural network (ANN) to replicate nonlinear spatio‐temporal discrete choice patterns produced by a spatially‐explicit ABM with social influence. This particular ABM has been developed to model consumer decision making between purchasing a Prius‐like hybrid or plug‐in hybrid electric vehicle (PHEV) for a given geographic region (e.g., city or town). Our goal is to see if an ANN trained at the city scale can operate as a “fast function approximator” to estimate nonlinear dynamic response functions (e.g., fleet distribution, environmental attitudes, etc.) based on city‐wide attributes (e.g., socio‐economic distributions). Recurrent feedback connections were added to the ANN to leverage the temporal history and correlations and improve forecasts in time. Outputs from the city‐scale ABM, run for a variety of population sizes and initial and input conditions, were used to train and test the ANN. Initial results suggest the ABM may be replaced by ANNs that interact with each other and other agents (e.g., manufacturing agents) to investigate PHEV penetration at the national scale.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).